Update nse.py

nse.py

@@ -1,395 +1,226 @@
-# ================================
-# NSE Fetch Module (DF Only)
-# ================================
-
-import datetime
-import pandas as pd
-import time
 import requests
-import nsepython # Moved import here
-#import nse
-HEADERS = {
-    "User-Agent": "Mozilla/5.0",
+import pandas as pd
+
+# ======================================================
+# Base Headers (mandatory for NSE API)
+# ======================================================
+NSE_HEADERS = {
+    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
     "Accept-Language": "en-US,en;q=0.9",
 }
 
-session = requests.Session()
-session.get("https://www.nseindia.com", headers=HEADERS, timeout=5)
 
-# ---------------------------------------------------
-# Helper: JSON Fetch
-# ---------------------------------------------------
-def fetch_data(url):
-    try:
-        response = session.get(url, headers=HEADERS, timeout=5)
-        response.raise_for_status()
-        return response.json()
-    except:
-        return None
-
-# ---------------------------------------------------
-# Clean DF
-# ---------------------------------------------------
-def clean_dataframe(df):
-    df.columns = df.columns.str.strip()
-    str_cols = df.select_dtypes(include=["object"]).columns
-    df[str_cols] = df[str_cols].apply(lambda x: x.str.strip())
-    df.fillna(0.01, inplace=True)
-    return df
-
-# ---------------------------------------------------
-# Bhavcopy Fetch → DataFrame
-# ---------------------------------------------------
-def nse_bhavcopy(date):
-    """Returns Cleaned Bhavcopy DF for EQ / BE / SM"""
-    date_str = date.strftime("%d-%m-%Y")
-    print(f"Attempting to fetch bhavcopy for date: {date_str}")
+# ======================================================
+# Helper: Convert DataFrame to HTML (single block)
+# ======================================================
+def _to_html(title, df):
+    if df is None or len(df) == 0:
+        return f"<h3>{title}</h3><p>No data available</p>"
+    return f"<h3>{title}</h3>" + df.to_html(index=False, border=1)
+
 
+# ======================================================================
+# STOCK QUOTE (similar to get_quote)
+# ======================================================================
+def nse_stock(symbol):
+    url = f"https://www.nseindia.com/api/quote-equity?symbol={symbol.upper()}"
     try:
-        df = nsepython.get_bhavcopy(date_str) # Direct call
-        if df is None or df.empty:
-            print(f"No bhavcopy data or empty DataFrame returned for {date_str}")
-            return None, None
+        r = requests.get(url, headers=NSE_HEADERS)
+        data = r.json()
 
-        actual_bhavcopy_date = datetime.datetime.strptime(
-            df.iloc[2, 2].strip(), "%d-%b-%Y"
-        ).date()
+        info = pd.json_normalize(data.get("info", {}))
+        price = pd.json_normalize(data.get("priceInfo", {}))
+        meta = pd.json_normalize(data.get("metadata", {}))
 
-        df = clean_dataframe(df)
-        df_filtered = df[df.iloc[:, 1].isin(["EQ", "BE", "SM"])]
+        html = ""
+        html += _to_html("Stock Info", info)
+        html += _to_html("Price Info", price)
+        html += _to_html("Metadata", meta)
 
-        return df_filtered, actual_bhavcopy_date
+        return html
 
     except Exception as e:
-        print(f"An error occurred while fetching bhavcopy for {date_str}: {e}")
-        return None, None
-
-# ---------------------------------------------------
-# Stock Deliverable DF (security-wise archive)
-# ---------------------------------------------------
-def nse_stock(nse_module, stock, start, end, series="ALL"):
-    """Return DF for security-wise archive (deliverable + all columns)"""
-
-    df = nse_module.security_wise_archive(start, end, stock, series)
-    if df is not None and not df.empty:
-        return df
-    return None
-
-# ---------------------------------------------------
-# All NSE Indices → DataFrames
-# ---------------------------------------------------
-def nse_indices():
-    url = "https://www.nseindia.com/api/allIndices"
-    data = fetch_data(url)
-    if data is None:
-        return None
-
-    # DataFrames
-    df_dates = pd.DataFrame([data["dates"]])
-    df_meta = pd.DataFrame([{k: v for k, v in data.items() if k not in ["data", "dates"]}])
-    df_data = pd.DataFrame(data["data"])
-
-    # Convert to HTML pieces
-    html_dates = df_dates.to_html(index=False, border=1)
-    html_meta  = df_meta.to_html(index=False, border=1)
-    html_data  = df_data.to_html(index=False, border=1)
-
-    # Combine into one single HTML block
-    full_html = (
-        "<h3>Dates</h3>" + html_dates +
-        "<br><h3>Meta</h3>" + html_meta +
-        "<br><h3>Data</h3>" + html_data
-    )
+        return f"<p>Error fetching stock: {e}</p>"
 
-    return full_html
 
-# ---------------------------------------------------
-# Specific Index → DataFrames
-# ---------------------------------------------------
-'''
-def open(index_name="NIFTY 50"):
-    url = f"https://www.nseindia.com/api/equity-stockIndices?index={index_name.replace(' ', '%20')}"
-    data = fetch_data(url)
-    if data is None:
-        return None
-
-    # Create DataFrames
-    df_market = pd.DataFrame([data["marketStatus"]])
-    df_adv    = pd.DataFrame([data["advance"]])
-    df_meta   = pd.DataFrame([data["metadata"]])
-    df_data   = pd.DataFrame(data["data"])
-
-    # Convert to HTML
-    html_market = df_market.to_html(index=False, border=1)
-    html_adv    = df_adv.to_html(index=False, border=1)
-    html_meta   = df_meta.to_html(index=False, border=1)
-    html_data   = df_data.to_html(index=False, border=1)
-
-    # Combine all into single HTML string
-    full_html = (
-        "<h3>Market Status</h3>" + html_market +
-        "<br><h3>Advance / Decline</h3>" + html_adv +
-        "<br><h3>Metadata</h3>" + html_meta +
-        "<br><h3>Index Data</h3>" + html_data
-    )
+# ======================================================================
+# OPTION CHAIN (similar to get_option_chain)
+# ======================================================================
+def nse_fno(symbol):
+    url = f"https://www.nseindia.com/api/option-chain-equities?symbol={symbol.upper()}"
+    try:
+        r = requests.get(url, headers=NSE_HEADERS)
+        data = r.json()
 
-    return full_html
+        records = data.get("records", {})
 
-'''
-import requests
-import json
+        all_data = pd.DataFrame(records.get("data", []))
+        ce_list = [x["CE"] for x in records.get("data", []) if "CE" in x]
+        pe_list = [x["PE"] for x in records.get("data", []) if "PE" in x]
+
+        df_ce = pd.DataFrame(ce_list)
+        df_pe = pd.DataFrame(pe_list)
+
+        html = ""
+        html += _to_html("F&O Combined", all_data)
+        html += _to_html("CALL Options (CE)", df_ce)
+        html += _to_html("PUT Options (PE)", df_pe)
 
-def fetch_data_from_nse(url):
-    headers = {"User-Agent": "Mozilla/5.0"}
+        return html
+
+    except Exception as e:
+        return f"<p>Error fetching FNO: {e}</p>"
+
+
+# ======================================================================
+# FUTURES DATA
+# ======================================================================
+def nse_future(symbol):
+    url = f"https://www.nseindia.com/api/quote-derivative?symbol={symbol.upper()}"
     try:
-        resp = requests.get(url, headers=headers)
-        if resp.status_code == 200:
-            return resp.json()
-    except:
-        return None
-
-# ---------------------------------------------------
-# Option Chain DF (Raw CE/PE)
-# ---------------------------------------------------
-def fetch_option_chain_df(symbol="NIFTY"):
-    url = f"https://www.nseindia.com/api/option-chain-indices?symbol={symbol}"
-    data = fetch_data(url)
-
-    if data and "filtered" in data:
-        ce_df = pd.DataFrame([r["CE"] for r in data["filtered"]["data"] if "CE" in r])
-        pe_df = pd.DataFrame([r["PE"] for r in data["filtered"]["data"] if "PE" in r])
-        return ce_df, pe_df
-
-    return None, None
-
-# ---------------------------------------------------
-# Pre-open market → DataFrame
-# ---------------------------------------------------
-def nse_preopen(key="NIFTY"):
-    url = f"https://www.nseindia.com/api/market-data-pre-open?key={key}"
-    data = fetch_data(url)
-    if not data:
-        return None
-
-    df = pd.DataFrame(data.get("data", []))
-
-    # Convert to one HTML table
-    html_table = df.to_html(index=False, border=1)
-
-    # Wrap into single block
-    full_html = "<h3>Pre-Open Market Data</h3>" + html_table
-
-    return full_html
-
-# ---------------------------------------------------
-# FNO Quote → DataFrames
-# ---------------------------------------------------
-def nse_fno(symbol):
-    payload = nsepython.nse_quote(symbol)
-    if not payload:
-        return None
-
-    # ---------- INFO ----------
-    info_keys = list(payload["info"].keys()) + [
-        "fut_timestamp",
-        "opt_timestamp",
-        "maxVolatility",
-        "minVolatility",
-        "avgVolatility",
-    ]
-
-    info_values = list(payload["info"].values()) + [
-        payload["fut_timestamp"],
-        payload["opt_timestamp"],
-        payload["underlyingInfo"]["volatility"][0]['maxVolatility'],
-        payload["underlyingInfo"]["volatility"][0]['minVolatility'],
-        payload["underlyingInfo"]["volatility"][0]['avgVolatility'],
-    ]
-
-    df_info = pd.DataFrame([info_values], columns=info_keys)
-
-    # ---------- MCAP ----------
-    df_mcap = pd.DataFrame(payload["underlyingInfo"].get("marketCap", {}))
-
-    # ---------- FNO LIST ----------
-    df_fno_list = pd.DataFrame(payload.get("allSymbol", []), columns=["FNO_Symbol"])
-
-    # ---------- STOCK DEPTH ----------
-    df_stock = process_stocks_df(payload["stocks"])
-
-    # Convert all to HTML
-    html_info = df_info.to_html(index=False, border=1)
-    html_mcap = df_mcap.to_html(index=False, border=1)
-    html_fno  = df_fno_list.to_html(index=False, border=1)
-    html_stock = df_stock.to_html(index=False, border=1)
-
-    # Combine into full HTML block
-    full_html = (
-        "<h3>FNO Info</h3>" + html_info +
-        "<br><h3>Market Cap</h3>" + html_mcap +
-        "<br><h3>FNO Symbol List</h3>" + html_fno +
-        "<br><h3>Stock Depth</h3>" + html_stock
-    )
+        r = requests.get(url, headers=NSE_HEADERS)
+        data = r.json()
 
-    return full_html
+        futures = pd.DataFrame(data.get("stocks", []))
+        meta = pd.json_normalize(data.get("info", {}))
 
-# ---------------------------------------------------
-# Handle nested stock → DF
-# ---------------------------------------------------
-def process_stocks_df(data):
-    """Return final merged stock DF only"""
-    trade_info_list, other_info_list = [], []
-    bid_ask_list = []
-    stock_values = []
-    trade_keys = other_keys = bidask_keys = stock_keys = None
+        html = ""
+        html += _to_html("Futures Data", futures)
+        html += _to_html("Metadata", meta)
 
-    for i, stock in enumerate(data):
-        meta = stock.pop("metadata")
-        depth = stock.pop("marketDeptOrderBook")
-        parent = stock
+        return html
 
-        trade_info = depth.pop("tradeInfo", {})
-        other_info = depth.pop("otherInfo", {})
+    except Exception as e:
+        return f"<p>Error fetching futures: {e}</p>"
 
-        trade_info_list.append(trade_info)
-        other_info_list.append(other_info)
 
-        # bid / ask
-        bid_ask_row = {}
-        for side in ["bid", "ask"]:
-            for j, entry in enumerate(depth.get(side, []), start=1):
-                bid_ask_row[f"{side}_price_{j}"] = entry.get("price")
-                bid_ask_row[f"{side}_qty_{j}"] = entry.get("quantity")
+# ======================================================================
+# 52-WEEK HIGH / LOW
+# ======================================================================
+def nse_high_low():
+    url = "https://www.nseindia.com/api/market-data-52Week"
+    try:
+        r = requests.get(url, headers=NSE_HEADERS)
+        data = r.json()
 
-        bid_ask_list.append(bid_ask_row)
+        high = pd.DataFrame(data.get("FiftyTwoWeekHigh", []))
+        low = pd.DataFrame(data.get("FiftyTwoWeekLow", []))
 
-        if i == 0:
-            trade_keys = list(trade_info.keys())
-            other_keys = list(other_info.keys())
-            bidask_keys = list(bid_ask_row.keys())
-            stock_keys = list(meta.keys()) + list(depth.keys()) + list(parent.keys())
+        html = ""
+        html += _to_html("52 Week High", high)
+        html += _to_html("52 Week Low", low)
 
-        stock_values.append(
-            list(meta.values()) + list(depth.values()) + list(parent.values())
-        )
+        return html
 
-    df_trade = pd.DataFrame(trade_info_list, columns=trade_keys)
-    df_other = pd.DataFrame(other_info_list, columns=other_keys)
-    df_bidask = pd.DataFrame(bid_ask_list, columns=bidask_keys)
-    df_stock = pd.DataFrame(stock_values, columns=stock_keys)
+    except Exception as e:
+        return f"<p>Error fetching high-low: {e}</p>"
+
+
+# ======================================================================
+# BHAVCOPY
+# ======================================================================
+def nse_bhav(date_str):
+    """
+    Supports input date format:
+    - DDMMYYYY
+    - DD-MM-YYYY
+    - DD/MM/YYYY
+    Converts automatically to DDMMYYYY for NSE API.
+    """
+
+    # ---------------------------
+    # Normalize date
+    # ---------------------------
+    try:
+        # Replace "-" or "/" with ""
+        clean = date_str.replace("-", "").replace("/", "")
 
-    df_stock = df_stock.drop(columns=['bid', 'ask', 'carryOfCost'], errors="ignore")
+        # Now clean should be DDMMYYYY
+        if len(clean) != 8:
+            return "<p>Error: Invalid date format. Use DDMMYYYY / DD-MM-YYYY / DD/MM/YYYY</p>"
 
-    return pd.concat([df_stock, df_trade, df_other, df_bidask], axis=1)
+        # Final API format = DDMMYYYY
+        api_date = clean
 
+    except Exception:
+        return "<p>Error: Unable to parse date.</p>"
 
+    # ---------------------------
+    # Fetch Data
+    # ---------------------------
+    url = (
+        "https://www.nseindia.com/api/reports"
+        f"?archives=true&date={api_date}&type=equities&mode=single"
+    )
 
+    try:
+        r = requests.get(url, headers=NSE_HEADERS)
+        data = r.json()
 
-#date = datetime.date(2025, 11, 27) # Trying a past date where data is likely available
+        df = pd.DataFrame(data.get("data", []))
 
-#df = nse_preopen_df("NIFTY")
-#df_bhav, act_date = fetch_bhavcopy_df(date)
-#df_ce, df_pe = fetch_option_chain_df("NIFTY")
-#df_m, df_a, df_meta, df_data = nse_index_df("NIFTY 50")
+        return _to_html(f"Bhavcopy {api_date}", df)
 
-#fno = nse_fno_df("RELIANCE")
+    except Exception as e:
+        return f"<p>Error fetching bhavcopy: {e}</p>"
 
 
-# -----------------------------
-# Global Variables
-# -----------------------------
-nse_del_key_map = {
-    'Symbol': "Symbol", 'Series': "Series",
-    'Date': 'Date', 'Prev Close': 'Preclose',
-    'Open Price': 'Open', 'High Price': 'High',
-    'Low Price': 'Low', 'Last Price': 'Last',
-    'Close Price': 'Close', 'Average Price': 'AvgPrice',
-    'Total Traded Quantity': 'Volume',
-    'Turnover ₹': 'Turnover', 'No. of Trades': "Trades",
-    'Deliverable Qty': "Delivery", '% Dly Qt to Traded Qty': "Del%"
-}
+# ======================================================================
+# ALL NSE INDICES LIST
+# ======================================================================
+def nse_indices():
+    url = "https://www.nseindia.com/api/allIndices"
+    try:
+        r = requests.get(url, headers=NSE_HEADERS)
+        data = r.json()
+
+        df = pd.DataFrame(data.get("data", []))
+
+        return _to_html("All NSE Indices", df)
+
+    except Exception as e:
+        return f"<p>Error fetching indices: {e}</p>"
 
-# -----------------------------
-# Data Fetching Functions (NSE)
-# -----------------------------
-def url_nse_del(symbol, start_date, end_date):
-    base_url = "https://www.nseindia.com/api/historicalOR/generateSecurityWiseHistoricalData"
-    start_date_str = start_date.strftime("%d-%m-%Y")
-    end_date_str = end_date.strftime("%d-%m-%Y")
-    url = f"{base_url}?from={start_date_str}&to={end_date_str}&symbol={symbol.split('.')[0]}&type=priceVolumeDeliverable&series=ALL&csv=true"
-    return url
-
-def to_numeric_safe(series):
-    series = series.replace('-', 0)
-    series = series.fillna(0)
-    series = series.astype(str).str.replace(',', '')
-    return pd.to_numeric(series, errors='coerce').fillna(0)
-
-def nse_daily(symbol, start_date_str=None, end_date_str=None):
-    # Default end date is today
-    end_date = datetime.now()
-    if end_date_str:
-        try:
-            end_date = datetime.strptime(end_date_str, "%Y-%m-%d")
-        except ValueError:
-            print(f"Warning: Invalid end date format '{end_date_str}'. Using today's date.")
-            end_date = datetime.now()
-
-    # Default start date is one year prior
-    start_date = end_date - timedelta(days=365)
-    if start_date_str:
-        try:
-            start_date = datetime.strptime(start_date_str, "%Y-%m-%d")
-        except ValueError:
-            print(f"Warning: Invalid start date format '{start_date_str}'. Using default start date.")
-            start_date = end_date - timedelta(days=365)
-
-    # Swap if needed
-    if start_date > end_date:
-        print("Warning: Start date is after end date. Swapping dates.")
-        start_date, end_date = end_date, start_date
-
-    url = url_nse_del(symbol, start_date, end_date)
-    headers = {"User-Agent": "Mozilla/5.0"}
 
+# ======================================================================
+# NSE OPEN MARKET DATA (same endpoint used in nsepython)
+# ======================================================================
+def nse_open(index_name):
+    url = f"https://www.nseindia.com/api/equity-stockIndices?index={index_name.replace(' ', '%20')}"
     try:
-        response = requests.get(url, headers=headers)
-        response.raise_for_status()
+        r = requests.get(url, headers=NSE_HEADERS)
+        data = r.json()
 
-        if not response.content:
-            return None
+        meta = pd.json_normalize(data.get("metadata", {}))
+        df = pd.DataFrame(data.get("data", []))
 
-        # Build DataFrame
-        df = pd.read_csv(io.StringIO(response.content.decode("utf-8"))).round(2)
-        df.columns = df.columns.str.strip()
-        df.rename(columns=nse_del_key_map, inplace=True)
+        html = ""
+        html += _to_html("Index Metadata", meta)
+        html += _to_html("Index Open Data", df)
 
-        # Capitalize column names
-        df.columns = [col.capitalize() for col in df.columns]
+        return html
 
-        # Remove unwanted columns
-        df.drop(columns=["Symbol", "Series", "Avgprice", "Last"], errors="ignore", inplace=True)
+    except Exception as e:
+        return f"<p>Error fetching open data: {e}</p>"
 
-        # Format date
-        df["Date"] = pd.to_datetime(df["Date"], format="%d-%b-%Y").dt.strftime("%Y-%m-%d")
 
-        # Ensure numeric columns
-        numeric_cols = ['Close', 'Preclose', 'Open', 'High', 'Low', 'Volume', 'Delivery', 'Turnover', 'Trades']
-        numeric_cols_cap = [c.capitalize() for c in numeric_cols]
+# ======================================================================
+# NSE PRE-OPEN MARKET DATA
+# ======================================================================
+def nse_preopen(index_name):
+    url = "https://www.nseindia.com/api/market-data-pre-open?key=NIFTY"
+    try:
+        r = requests.get(url, headers=NSE_HEADERS)
+        data = r.json()
 
-        for col in numeric_cols_cap:
-            if col in df.columns:
-                df[col] = to_numeric_safe(df[col])
-            else:
-                df[col] = 0
+        df = pd.DataFrame(data.get("data", []))
+        meta = pd.json_normalize(data.get("metadata", {}))
 
-        # Convert to HTML
-        html_table = df.to_html(index=False, border=1)
+        html = ""
+        html += _to_html("Pre-Open Metadata", meta)
+        html += _to_html("Pre-Open Market Data", df)
 
-        full_html = "<h3>Daily Data</h3>" + html_table
-        return full_html
+        return html
 
     except Exception as e:
-        print(f"Error fetching data from NSE for {symbol}: {e}")
-        return None
-
+        return f"<p>Error fetching preopen: {e}</p>"